Development of the exploration method and integrated interpretation tool for Seafloor Massive Sulfide（SMS）

Tara, Kenji; Kawai, Nobuo

doi:10.3720/japt.84.85

Cited by 1 publication

(1 citation statement)

References 5 publications

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“…Immediate identification of the source of material dispersion from limited information in the ocean or atmosphere even under turbulent conditions is an important task (Hutchinson et al, 2017;Francis et al, 2022). For instance, in the search for hydrothermal deposits as seafloor mineral resources, their locations must be estimated based on limited local observations in the harsh environment of the deep ocean (Boschen et al, 2013;Tara and Kawai, 2019;Morato et al, 2022). Chemical plant safety would be enhanced by the immediate identification of the gas leakage source from limited detector information (Shu et al, 2016;Shen et al, 2020;Fahimipirehgalin et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Deep learning estimation of scalar source distance for different turbulent and molecular diffusion environments

TSUKAHARA,

ISHIGAMI,

IRIKURA

2024

JFST

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In order to adopt convolutional neural networks (CNN) for practical use in estimating the source of scalar dispersion in turbulent flows, such as gas leaks in industrial plants, the inference accuracy was verified using scalar concentration distributions in various turbulent environmental conditions. Training and test data were obtained through quasi direct numerical simulations on a flow system with a scalar-source-attached cylinder downstream of a turbulent grid, at two Schmidt numbers. An inference accuracy of at least 90% was confirmed under trained flow conditions, provided that the observation window was large enough to capture the integral scale in scalar fluctuations in turbulence. When the flow conditions (in terms of the turbulence intensity and the Schmidt number) differed between the training and testing phases, the accuracy was significantly reduced. However, the learner supervised with images under all different conditions showed high generalization performance. Singular value decomposition was used to discuss the features essential for training and prediction. In our CNN inference, we concluded that the source estimation was achieved by extracting from the integral-scale scalar patch distribution the features related to each of the turbulent and molecular diffusion progressions and their correlations. The results provide insights into the potential solutions for accurately predicting the sources of material dispersion in turbulent environments.

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